Recent trends indicate that the future will progress towards sensor-actuator based automation in various sectors including buildings, communities/cities, transportation, energy, etc. Experts predict that in the coming decades there will be a fabric of trillions of sensor-actuator devices embedded into our surroundings. This sensor-actuator fabric (SAF) will bring about integrated automation that will greatly improve the efficiency of the environment and/or resources, as well as the quality of living for those within the environment. A SAF may be comprised of thousands, millions, or even trillions of electronic devices/nodes that interact with one another in the context of a connected environment. The electronic devices/nodes within a SAF typically have a finite lifetime and/or less than 100% reliability. In view of the interconnected nature of a SAF, a fault in, or failure of, an electronic device/node within the fabric is problematic because it may lead to a fault/misbehavior of the fabric that may lead to catastrophic consequences.
Robust techniques of fault detection and root cause analysis are thus generally desired in order to maintain data transmission reliability and control delays within a SAF. Conventional practice for determining the root cause of a fault within a SAF is to monitor a pre-defined list of performance metrics for unexpected degradations and/or behaviors. However, since there are potentially thousands of time-series metrics that can be created from the available data within a SAF, the amount of data subject to root cause analysis may be massive. Moreover, root cause analysis of such data is typically performed manually, which is cumbersome, error-prone, and difficult/impossible to scale.